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By Cecilio Angulo, Lluis Godo

We suggest 2 month, 10 guy learn of synthetic intelligence be performed throughout the summer time of 1956 at Dartmouth collage in Hanover, New Hampshire. The research is to proceed.... final 12 months the fiftieth anniversary of the Dartmouth AI venture thought via McCarthy, Minsky, Rochester and Shannon was once celebrated. Years later, and following comparable traditions of a few AI institutions, a decision was once introduced in 1997 by means of the Catalan organization for man made Intelligence (ACIA) to arrange an annual convention to advertise synergies within the learn neighborhood of its effect, the seeder for the first Catalan convention on synthetic Intelligence (CCIA98) which came about in Tarragona on October 1998. The editors are very completely happy to rejoice the tenth anniversary of the foreign convention of the ACIA (CCIA07) in Sant Julià de Lòria (Andorra), October 2526th, 2007. the nice well-being of the Catalan AI neighborhood and its effect sector is witnessed via the consultant choice of papers accrued during this booklet and offered at CCIA07. The booklet is equipped in keeping with the several components during which the papers have been disbursed for his or her presentation throughout the convention, particularly: Constraint pride, brokers, information Processing, Case-Based Reasoning, laptop imaginative and prescient, ordinary Language Processing, Uncertainty and Fuzziness, Robotics, and purposes. The editors think that each one the papers amassed during this quantity could be of curiosity to any desktop scientist or engineer drawn to AI.

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Berlin-Dahlem: Konrad-Zuse-Zentrum für Informationstechnik, 2005. [2] P. Chu and J. Beasley. A genetic algorithm for the set partitioning problem. Imperial College, London, 1995. [3] Niklas Kohl and Stefan E. Karisch. Airline crew rostering: Problem types, modeling and optimizacion. Kluwer Academic Publishers, Annals of Operations Research 127, 223-257, 2004. [4] B. López. Time control in road passenger transportation. problem description and formalization. Research Report IIiA 05-04, University of Girona, 2005.

In this paper, we introduce soft constraint network models, the problem of determining the minimum number of errors needed to explain the data and the problem of proposing an optimal correction to an error. In Section 2, we describe the algorithms used to solve these problems. We report extensive results using the weighted constraint network solver toulbar2 and other solvers in Section 3. 1. Modeling the problems A Weighted Constraint Network (WCN) (X , D , C ) [6] is defined by a set of n variables X = {x1 , .

The complexity of ternary EDAC is time O(ed 3 max{nd, }) and space O(ed 2 ), where n is the number of variables, d is the maximum domain size, e is the number of constraints and is the maximum cost. The proof can be found in [17]. We maintain EDAC during search, producing a lower bound in C∅ . The DAC variable ordering corresponds to the pedigree file order, which is usually a temporal order. If C∅ ≥ then, the algorithm backtracks. We use dynamic variable and value ordering heuristics. e. its assignment results in an empty domain or C∅ ≥ ) [10].

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